Visualize Location Analysis for Yelp Businesses in Pittsburgh Area (Beta Version)

library(ggmap)

## Warning: package 'ggmap' was built under R version 3.4.4

## Loading required package: ggplot2

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## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

library(ggplot2)
library(htmlwidgets)

## Warning: package 'htmlwidgets' was built under R version 3.4.4

library(magrittr)

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library(leaflet)

## Warning: package 'leaflet' was built under R version 3.4.4

library(dplyr)

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library(tibble)

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library(shiny)

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library(leaflet.extras)

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library(jsonlite)

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## Attaching package: 'jsonlite'

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yelp <- stream_in(file("business.json"))

## opening file input connection.

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## closing file input connection.

yelp_flat <- flatten(yelp)
yelp_data <- as_data_frame(yelp_flat)
yelp_data <- yelp_data[which(yelp_data$review_count>=50),]
yelp_data[yelp_data==""]<-NA

drop <- c("hours.Monday","hours.Tuesday","hours.Wednesday","hours.Thursday","hours.Friday","hours.Saturday","hours.Sunday","attributes.AcceptsInsurance","attributes.Open24Hours","attributes.AgesAllowed","attributes.BYOB","attributes.HairSpecializesIn","attributes.Corkage","attributes.DriveThru","attributes.ByAppointmentOnly","attributes.BYOBCorkage","attributes.DogsAllowed","attributes.Smoking","attributes.CoatCheck","attributes.BusinessParking","attributes.Ambience","attributes.GoodForMeal","attributes.Music","attributes.BestNights","attributes.WheelchairAccessible","attributes.BusinessAcceptsBitcoin","attributes.GoodForDancing","attributes.RestaurantsAttire","attributes.NoiseLevel","attributes.Alcohol","attributes.HappyHour","attributes.RestaurantsTakeOut","attributes.RestaurantsDelivery","attributes.RestaurantsReservations","attributes.BusinessAcceptsCreditCards","attributes.RestaurantsGoodForGroups","attributes.GoodForKids","attributes.HasTV","attributes.OutdoorSeating","attributes.WiFi","attributes.RestaurantsTableService","attributes.Caters","attributes.BikeParking")
yelp_data <- yelp_data[,!(names(yelp_data) %in% drop)]
sort(colSums(is.na(yelp_data)),decreasing = T)

## attributes.RestaurantsPriceRange2                           address 
##                                55                                 8 
##                       business_id                              name 
##                                 0                                 0 
##                              city                             state 
##                                 0                                 0 
##                       postal_code                          latitude 
##                                 0                                 0 
##                         longitude                             stars 
##                                 0                                 0 
##                      review_count                           is_open 
##                                 0                                 0 
##                        categories 
##                                 0

completeFun <- function(data, desiredCols) {
  completeVec <- complete.cases(data[, desiredCols])
  return(data[completeVec, ])
}
yelp_data <- completeFun(yelp_data, "attributes.RestaurantsPriceRange2")
yelp_data <- completeFun(yelp_data, "address")

biz <- yelp_data
head(biz)

## # A tibble: 6 x 13
##   business_id name  address city  state postal_code latitude longitude
##   <chr>       <chr> <chr>   <chr> <chr> <chr>          <dbl>     <dbl>
## 1 dQj5DLZjeD… Apte… 4606 P… Pitt… PA    15224           40.5     -79.9
## 2 v-scZMU6jh… No. … 436 Ma… Pitt… PA    15222           40.4     -80.0
## 3 CU047MuCn7… Carn… 4400 F… Pitt… PA    15213           40.4     -80.0
## 4 F6c3D1o9Z4… Hell… 701 Pr… Pitt… PA    15237           40.6     -80.0
## 5 JrrY4v21k7… Kell… 6012 P… Pitt… PA    15206           40.5     -79.9
## 6 sMzNLdhJZG… Sien… 942 Pe… Pitt… PA    15222           40.4     -80.0
## # ... with 5 more variables: stars <dbl>, review_count <int>,
## #   is_open <int>, categories <chr>,
## #   attributes.RestaurantsPriceRange2 <chr>

register_google(key = "your-key")

qmap(location = "pittsburgh") 

## Source : https://maps.googleapis.com/maps/api/staticmap?center=pittsburgh&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx

## Warning in strptime(x, fmt, tz = "GMT"): unknown timezone 'zone/tz/2019a.
## 1.0/zoneinfo/America/New_York'

## Source : https://maps.googleapis.com/maps/api/geocode/json?address=pittsburgh&key=xxx

ggmap(
  ggmap = get_map(
     c(lon = mean(biz$longitude), lat = mean(biz$latitude)),
     zoom = 11, 
     scale = "auto",
     maptype = "terrain", #"terrain", "terrain-background", "satellite", "roadmap", and "hybrid" (google maps), "terrain", "watercolor", and "toner"
     source = "google"),
  extent = "device",
  legend = "topright"
  ) + geom_point(data = biz, aes(x = biz$longitude, y = biz$latitude), alpha = 0.5, color = "red") + theme(legend.position = "right") + 
  labs(
    x = "Longitude", 
    y = "Latitude", 
    title = "Yelp - Pittsburgh")

## Source : https://maps.googleapis.com/maps/api/staticmap?center=40.446394,-79.981998&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx

## Warning: Removed 5 rows containing missing values (geom_point).

Assumption: the potential merchant wants to start up a restaurant with

- more than average of stars (3.76),

- review count (145) and

- more than median of price range (2).

Pull yelp sites with more than average stars in Pittsburgh

# Pull yelp sites with more than average stars in Pittsburgh
avg <- mean(biz$stars) 
pittTopStarFrame = biz %>% filter(stars > avg) # 543

## Warning: package 'bindrcpp' was built under R version 3.4.4

content <- paste("Name:", pittTopStarFrame$name, "<br>",
                 "Address:", pittTopStarFrame$address, "<br>",
                 "Postal Code:", pittTopStarFrame$postal_code, "<br>",
                 "Categories:", pittTopStarFrame$categories, "<br>",
                 "Star:", pittTopStarFrame$stars)
pal <- colorNumeric(
  palette = "YlOrRd", #https://www.r-bloggers.com/palettes-in-r/
  domain = pittTopStarFrame$stars
)

leaflet(pittTopStarFrame) %>% addTiles() %>% 
  setView(lng = mean(pittTopStarFrame$longitude), lat = mean(pittTopStarFrame$latitude), zoom = 11) %>% 
  addCircleMarkers(lng = ~pittTopStarFrame$longitude, lat = ~pittTopStarFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content)

Pull yelp sites with more than average review count in Pittsburgh

# Pull yelp sites with more than average review count in Pittsburgh
avg1 <- mean(biz$review_count) 
pittTopReviewCountFrame = biz %>% filter(review_count > avg1) # 297
content <- paste("Name:", pittTopReviewCountFrame$name, "<br>",
                 "Address:", pittTopReviewCountFrame$address, "<br>",
                 "Postal Code:", pittTopReviewCountFrame$postal_code, "<br>",
                 "Categories:", pittTopReviewCountFrame$categories, "<br>",
                 "Review Count:", pittTopReviewCountFrame$review_count)
pal <- colorNumeric(
  palette = "YlOrRd", #https://www.r-bloggers.com/palettes-in-r/
  domain = pittTopReviewCountFrame$review_count
)

leaflet(pittTopReviewCountFrame) %>% addTiles() %>% 
  setView(lng = mean(pittTopReviewCountFrame$longitude), lat = mean(pittTopReviewCountFrame$latitude), zoom = 11) %>% 
  addCircleMarkers(lng = ~pittTopReviewCountFrame$longitude, lat = ~pittTopReviewCountFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content)

Pull yelp sites with more than median price range in Pittsburgh

# Pull yelp sites with more than median price range in Pittsburgh
med <- median(as.numeric(biz$attributes.RestaurantsPriceRange2))
pittTopPriceRangeFrame = biz %>% filter(attributes.RestaurantsPriceRange2 > med) # 297
content <- paste("Name:", pittTopPriceRangeFrame$name, "<br>",
                 "Address:", pittTopPriceRangeFrame$address, "<br>",
                 "Postal Code:", pittTopPriceRangeFrame$postal_code, "<br>",
                 "Categories:", pittTopPriceRangeFrame$categories, "<br>",
                 "Price Range:", pittTopPriceRangeFrame$attributes.RestaurantsPriceRange2)
pal <- colorNumeric(
  palette = "YlOrRd", #https://www.r-bloggers.com/palettes-in-r/
  domain = as.numeric(pittTopPriceRangeFrame$attributes.RestaurantsPriceRange2)
)

leaflet(pittTopPriceRangeFrame) %>% addTiles() %>% 
  setView(lng = mean(pittTopPriceRangeFrame$longitude), lat = mean(pittTopPriceRangeFrame$latitude), zoom = 11) %>% 
  addCircleMarkers(lng = ~pittTopPriceRangeFrame$longitude, lat = ~pittTopPriceRangeFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content)

Combination of the Scenarios shown above

content_stars <- paste("Name:", pittTopStarFrame$name, "<br>",
                 "Address:", pittTopStarFrame$address, "<br>",
                 "Postal Code:", pittTopStarFrame$postal_code, "<br>",
                 "Categories:", pittTopStarFrame$categories, "<br>",
                 "Review Count:", pittTopStarFrame$review_count, "<br>",
                 "Price Range:", pittTopStarFrame$attributes.RestaurantsPriceRange2, "<br>",
                 "Star:", pittTopStarFrame$stars)

content_review_count <- paste("Name:", pittTopReviewCountFrame$name, "<br>",
                 "Address:", pittTopReviewCountFrame$address, "<br>",
                 "Postal Code:", pittTopReviewCountFrame$postal_code, "<br>",
                 "Categories:", pittTopReviewCountFrame$categories, "<br>",
                 "Review Count:", pittTopReviewCountFrame$review_count, "<br>",
                 "Price Range:", pittTopReviewCountFrame$attributes.RestaurantsPriceRange2, "<br>",
                 "Star:", pittTopReviewCountFrame$stars)

content_price_range <- paste("Name:", pittTopPriceRangeFrame$name, "<br>",
                 "Address:", pittTopPriceRangeFrame$address, "<br>",
                 "Postal Code:", pittTopPriceRangeFrame$postal_code, "<br>",
                 "Categories:", pittTopPriceRangeFrame$categories, "<br>",
                 "Review Count:", pittTopPriceRangeFrame$review_count, "<br>",
                 "Price Range:", pittTopPriceRangeFrame$attributes.RestaurantsPriceRange2, "<br>",
                 "Star:", pittTopPriceRangeFrame$stars)

pal <- colorNumeric(
  palette = "YlOrRd", #https://www.r-bloggers.com/palettes-in-r/
  domain = as.numeric(biz$attributes.RestaurantsPriceRange2)
)

leaflet() %>% addTiles() %>% 
  setView(lng = mean(biz$longitude), lat = mean(biz$latitude), zoom = 11) %>% 
  addProviderTiles("Esri.WorldImagery", group="background 1") %>%
  addTiles(options = providerTileOptions(noWrap = TRUE), group="background 2") %>%
  addCircleMarkers(data=pittTopStarFrame, lng = ~pittTopStarFrame$longitude, lat = ~pittTopStarFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content_stars, group="star") %>%
  addCircleMarkers(data=pittTopReviewCountFrame, lng = ~pittTopReviewCountFrame$longitude, lat = ~pittTopReviewCountFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content_review_count, group="review_count") %>%
  addCircleMarkers(data=pittTopPriceRangeFrame, lng = ~pittTopPriceRangeFrame$longitude, lat = ~pittTopPriceRangeFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content_price_range, group="price_range") %>%
  addLayersControl(overlayGroups = c("star","review_count","price_range") , baseGroups = c("background 1","background 2"), options = layersControlOptions(collapsed = FALSE))

Search for Name, Zip Code and Categories to help potential merchants understand the competitors

content <- paste("Name:", biz$name, "<br>",
                 "Address:", biz$address, "<br>",
                 "Postal Code:", biz$postal_code, "<br>",
                 "Categories:", biz$categories, "<br>",
                 "Price Range:", biz$attributes.RestaurantsPriceRange2, "<br>",
                 "Review Count:", biz$review_count, "<br>",
                 "Star:", biz$stars)

leaflet(biz) %>% addProviderTiles(providers$OpenStreetMap) %>%
  addCircles(data=biz, lng = ~biz$longitude, lat = ~biz$latitude, weight = 1, fillOpacity = 0.5, 
             popup = ~content, label = ~biz$name, group = "name") %>%  
  addCircles(data=biz, lng = ~biz$longitude, lat = ~biz$latitude, weight = 1, fillOpacity = 0.5, 
             popup = ~content, label = ~biz$postal_code, group = "zip") %>%
  addCircles(data=biz, lng = ~biz$longitude, lat = ~biz$latitude, weight = 1, fillOpacity = 0.5, 
             popup = ~content, label = ~biz$categories, group = "cat") %>%  
  addResetMapButton() %>%
  addSearchFeatures(targetGroups = c("name","zip","cat"),options = searchFeaturesOptions(zoom = 12, openPopup = TRUE, firstTipSubmit = TRUE, autoCollapse = TRUE, hideMarkerOnCollapse = TRUE )) %>%
  addControl("<P><B>Hint!</B> Search for ...<br/><ul><li>Name of Restaurant</li><li>Zip Code</li><li>Categories</li></ul></P>",position = "bottomright")

Statistical Analysis along with Visualization (Popularity Analysis)

pal <- colorNumeric(
  palette = "YlOrRd", #https://www.r-bloggers.com/palettes-in-r/
  domain = pittTopStarFrame$stars
)

server <- function(input, output) {
    # create a reactive value that will store the click position
    data_of_click <- reactiveValues(clickedMarker=NULL)

    # Leaflet map with 2 markers
    output$map <- renderLeaflet({

      leaflet() %>%
        addProviderTiles(providers$Esri.WorldStreetMap) %>%
        addResetMapButton() %>%
        addSearchOSM() %>%
        addSearchGoogle() %>%
        addSearchUSCensusBureau()
    
      # Reverse Geocoding using OSM
      leaflet()  %>%
        addProviderTiles(providers$OpenStreetMap) %>%
        addResetMapButton() %>%
        addReverseSearchOSM()
      
      leaflet(biz) %>% addTiles() %>% 
          setView(lng = mean(biz$longitude), lat = mean(biz$latitude), zoom = 11) %>% 
          addProviderTiles("Esri.WorldImagery", group="background 1") %>%
          addTiles(options = providerTileOptions(noWrap = TRUE), group="background 2") %>%
          addCircleMarkers(data=pittTopStarFrame, lng = ~pittTopStarFrame$longitude, lat = ~pittTopStarFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content_stars, group="star") %>%
          addCircleMarkers(data=pittTopReviewCountFrame, lng = ~pittTopReviewCountFrame$longitude, lat = ~pittTopReviewCountFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content_review_count, group="review_count") %>%
          addCircleMarkers(data=pittTopPriceRangeFrame, lng = ~pittTopPriceRangeFrame$longitude, lat = ~pittTopPriceRangeFrame$latitude, clusterOptions = markerClusterOptions(), popup = ~content_price_range, group="price_range") %>%
          addLayersControl(overlayGroups = c("star","review_count","price_range") , baseGroups = c("background 1","background 2"), options = layersControlOptions(collapsed = FALSE))
      })

    # store the click
    observeEvent(input$map_marker_click,{
    data_of_click$clickedMarker <- input$map_marker_click
    })

    # Make a barplot depending of the selected point
    output$plot=renderPlot({
      my_place=data_of_click$clickedMarker$id
      barplot(rnorm(10), col=rgb(0.1,0.4,0.9,0.3))
    })
}

ui <- fluidPage(
    br(),
    column(8,leafletOutput("map", height="600px")),
    column(4,br(),br(),br(),br(),plotOutput("plot", height="300px")),
    br()
)

shinyApp(ui = ui, server = server)

Shiny applications not supported in static R Markdown documents

library(rsconnect)

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rsconnect::deployApp('Yelp_Visualize.Rmd')

## Discovering document dependencies...

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To Be Continued …